Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Plane Electromagnetic Waves I01:30

Plane Electromagnetic Waves I

4.4K
The existence of combined electric and magnetic fields that propagate through space as electromagnetic (EM) waves is the most significant prediction of Maxwell's equations. As Maxwell's equations hold in free space, the predicted electromagnetic waves do not require a medium for their propagation. An EM wave comprises an electric field, defined as the force per charge on a stationary charge, and a magnetic field, which is the force per charge on a moving charge.
The EM field is assumed...
4.4K
Plane Electromagnetic Waves II01:29

Plane Electromagnetic Waves II

3.7K
Consider a plane wavefront traveling in position x-direction with a constant speed. This wavefront can be utilized to obtain the relationship between electric and magnetic fields with the help of Faraday's law.
3.7K
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

357
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
357
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

1.2K
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
1.2K
Propagation Speed of Electromagnetic Waves01:30

Propagation Speed of Electromagnetic Waves

4.1K
Electromagnetic waves are consistent with Ampere's law. Assuming there is no conduction current Ampere's law is given as:
4.1K
Sampling Plans01:23

Sampling Plans

346
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
346

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Precoder Design for Network Massive MIMO Optical Wireless Communications.

Sensors (Basel, Switzerland)·2024
Same author

Optimal DCO-OFDM signal shaping with double-sided clipping in visible light communications.

Optics express·2020
Same author

Satellite-Aided Consensus Protocol for Scalable Blockchains.

Sensors (Basel, Switzerland)·2020
Same author

Asymptotic BER analysis of FSO with multiple receive apertures over ℳ -distributed turbulence channels with pointing errors.

Optics express·2014
Same author

[New advance of research on prognostic factors in myelodysplastic syndrome--review].

Zhongguo shi yan xue ye xue za zhi·2008
Same author

[Report of a case with pure red cell aplasia following recombinant human erythropoietin treatment].

Zhonghua er ke za zhi = Chinese journal of pediatrics·2008
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Oct 25, 2025

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

7.9K

Broad Coverage Precoding for 3D Massive MIMO with Huge Uniform Planar Arrays.

An-An Lu1,2, Yan Chen1, Xiqi Gao1,2

  • 1National Mobile Communications Research Laboratory (NCRL), Southeast University, Nanjing 210096, China.

Entropy (Basel, Switzerland)
|August 6, 2021
PubMed
Summary
This summary is machine-generated.

We developed a new precoder for 3D massive MIMO systems using uniform planar arrays. This method significantly reduces computational complexity while maintaining performance, making 3D massive MIMO more efficient.

Keywords:
broad coverage precoder designhigh dimensionmassive multi-input multi-output (MIMO)uniform planar array (UPA)

More Related Videos

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface
06:14

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

Published on: July 30, 2020

5.1K
Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
09:43

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

10.1K

Related Experiment Videos

Last Updated: Oct 25, 2025

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

7.9K
Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface
06:14

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

Published on: July 30, 2020

5.1K
Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
09:43

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

10.1K

Area of Science:

  • Wireless communication
  • Signal processing
  • Antenna theory

Background:

  • Three-dimensional (3D) massive multi-input multi-output (MIMO) systems with huge uniform planar arrays (UPAs) are crucial for future wireless networks.
  • Traditional broad coverage precoder design faces high-dimensional optimization challenges.
  • The separable two-dimensional (2D) angle power spectrum is a common assumption.

Purpose of the Study:

  • To propose a novel broad coverage precoder design for 3D massive MIMO systems.
  • To reduce the computational complexity of precoder design.
  • To maintain performance comparable to existing methods.

Main Methods:

  • The proposed method decomposes high-dimensional precoding matrices into low-dimensional orthonormal vectors and pairs of vectors.
  • It utilizes per-antenna constant power and semi-unitary constraints.
  • Optimization techniques are employed to generate the required vectors.

Main Results:

  • The proposed precoder design effectively reduces the optimization space dimensionality.
  • Simulation results demonstrate performance close to normal broad coverage precoders.
  • A significant reduction in computational complexity was achieved.

Conclusions:

  • The novel precoder design offers a computationally efficient solution for 3D massive MIMO systems.
  • It provides a practical approach for deploying massive MIMO with UPAs.
  • The method balances performance and complexity for enhanced wireless communication.